# coding=utf-8

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import argparse
import sys
import os
import tensorflow as tf
import cv2
def deepnn(x):
    x_image = tf.reshape(x, [-1, 28, 28, 1])
    W_conv1 = weight_variable([5, 5, 1, 32])

    b_conv1 = bias_variable([32])
    h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
    h_pool1 = max_pool_2x2(h_conv1)

    # Second convolutional layer -- maps 32 feature maps to 64.
    W_conv2 = weight_variable([5, 5, 32, 64])
    b_conv2 = bias_variable([64])
    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)

    # Second pooling layer.
    h_pool2 = max_pool_2x2(h_conv2)

    # Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image
    W_fc1 = weight_variable([7 * 7 * 64, 1024])
    b_fc1 = bias_variable([1024])

    h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
    h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

    # Dropout - controls the complexity of the model, prevents co-adaptation of
    # features.
    keep_prob = tf.placeholder(tf.float32,name="prob")
    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

    # Map the 1024 features to 10 classes, one for each digit
    W_fc2 = weight_variable([1024, 11])
    b_fc2 = bias_variable([11])

    y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
    return y_conv, keep_prob


def conv2d(x, W):
    """conv2d returns a 2d convolution layer with full stride."""
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')


def max_pool_2x2(x):
    """max_pool_2x2 downsamples a feature map by 2X."""
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
                          strides=[1, 2, 2, 1], padding='SAME')


def weight_variable(shape):
    """weight_variable generates a weight variable of a given shape."""
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)


def bias_variable(shape):
    """bias_variable generates a bias variable of a given shape."""
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)

#mnist = input_data.read_data_sets('input_data', one_hot=True)

x = tf.placeholder(tf.float32, [None, 784],name="img")

y_ = tf.placeholder(tf.float32, [None, 11])

y_conv, keep_prob = deepnn(x)

cross_entropy = tf.reduce_mean(
    tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
predict=tf.argmax(y_conv,1,name="predict")
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

#prepare data
files = os.listdir("train_data/")
print(len(files))
imgs = []
ys = []
for file in files:
    img = cv2.imread("train_data/" + file)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    _, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)

    thresh = thresh.reshape((784)).tolist()
    thresh = np.minimum(thresh, 1)
    imgs.append(thresh)
    if file[0] == "n":
        ys.append(10)
    else:
        ys.append(int(file[0]))
imgs = np.array(imgs, dtype="float")
labels = np.zeros((len(ys), 11))
for i, y in enumerate(ys):
    labels[i][y] = 1

# daluan shuju
permutation = np.random.permutation(labels.shape[0])
imgs = imgs[permutation, :]
labels = labels[permutation, :]
print(imgs.shape, labels.shape)

#train data test data
train_imgs=imgs[:-100]
train_labels=labels[:-100]
test_imgs=imgs[-100:]
test_labels=labels[-100:]

saver=tf.train.Saver()
batch=20
total=train_imgs.shape[0]
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for i in range(10000):
        batch_xs = train_imgs[(i * batch) % total:((i + 1) * batch) % total].reshape(-1, 784)
        batch_ys = train_labels[(i * batch) % total:((i + 1) * batch) % total].reshape(-1, 11)
        #batch = mnist.train.next_batch(50)
        #print(batch[0].shape,batch[1].shape)
        if i % 100 == 0:
            train_accuracy = accuracy.eval(feed_dict={
                x: batch_xs, y_: batch_ys, keep_prob: 1.0})
            print('step %d, training accuracy %g' % (i, train_accuracy))
        train_step.run(feed_dict={x: batch_xs, y_: batch_ys, keep_prob: 0.5})
    saver.save(sess, 'checkpoint2/model')
    print('test accuracy %g' % accuracy.eval(feed_dict={
        x: test_imgs, y_: test_labels, keep_prob: 1.0}))
